1. Field of the Invention
The present invention relates to a method of applying a user-defined inference rule in an ontology-based knowledge base management system. More particularly, the present invention relates to a user-defined rule inference enabling method using only a function of searching a knowledge base as a base function in a knowledge base management system that does not apply rule operations in a form of IF-THEN statements, similar to an inference engine based on a Tableaux algorithm.
This work was supported by the IT R&D program of MIC/IITA [2006-S-026-02, Development of the URC Server Framework for Proactive Robotics Services].
2. Description of the Related Art
As a next-generation version of a database technology that represents and uses data on the basis of a relational model, various researches have been actively performed on a knowledge representation technology that can naturally represent knowledge in the real world and automatically derive new knowledge using a logic inference. A representative technology that has been developed is a Semantic Web technology.
The Semantic Web is the next-generation intelligent Web through which a computer can understand the meaning of knowledge resources and perform a logic inference. The Semantic Web is a framework that represents information on resources (Web documents, various files, services, and the like) and relatedness/meaning information (Semantics) between the resources in a distribution environment, such as the Internet, in an ontology form to be processed by a machine (computer), and allows an automated machine (computer) to process the information. Research on the Semantic Web is actively performed with priority given to an RDF (Resource Description Framework)-based ontology technology.
Among the ontology-based knowledge representation technologies, knowledge representation technologies based on description logic can ensure soundness and completeness of a logic inference. As a representative knowledge representation technology based on description logic, there is a Web ontology language (OWL) that is suggested by the Semantic Web.
A system that draws an inference on knowledge represented on the basis of description logic may be implemented by various methods. Among them, a general method is a method of drawing an inference using rules in a form of IF-THEN statements. For example, a paper titled “Implementation and Application of Ontology Databases with User-Defined Rules (UDR) Supported” (Preceedings of the First International Conference on Semantics, Knowledge, and Grod (SKG 2005), p. 82, November 2005) by Zang Zuling and Wang Hongbing discloses a method of drawing an inference on rules in a form of IF-THEN statements using an existing database system.
In this paper, instead of a RDF or OWL-based knowledge base, a table structure of a database is used as knowledge, and a technology that simulates an inference engine as a function based on UDR (User Defined Rules) is described. That is, when an attribute table and a rule table are created in a general database and a search is executed, new attribute data is added from the rule table through a recursive call of the UDR-based function. In this method, an existing database and the stored UDR are used to simulate a description logic inference as well as a user-defined rule inference in a form of IF-THEN statements.
However, as described above, in simulating an inference process using rules in a form of IF-THEN statements and recursive applications thereof, the following problems occur.
In the above-described method, a description logic inference engine is not used. Accordingly, a description logic inference process also needs to be created according to a user-defined rule, and whenever a knowledge search is requested, a user-defined inference rule for simulating the description logic inference process and a user-defined inference rule created according to a request from a user need to be applied and executed.
When rule operations in a form of IF-THEN statements are performed, this is to simulate the description logic inference process by a chain application of a plurality of rules, which results in an infinite loop occurring due to a recursive call. In order to prevent this problem, the paper suggests a method in which an inference depth is set. However, if this method is applied, it is not possible to ensure completeness of the inference result. That is, there is a problem in that it is not possible to satisfy completeness and decidability of the inference result at the same time.
In order to solve these problems, in recent years, a Tableaux algorithm, which implements a description logic inference process by a software algorithm, is developed, and a general ontology-based knowledge base management system supports an inference based on a Tableaux algorithm.
The Tableaux algorithm shows being “unsatisfiable” by applying various conversion rules with respect to “not” of a content to be verified, and uses a method which verifies that the content to be verified is satisfiable. That is, the Tableaux algorithm shows that “not” of the content to be verified is unsatisfiable by searching all search spaces, and interferes being satisfiable. Examples of an inference engine that implements the Tableaux algorithm include FaCT, FaCT++, Pellet, and RacerPro. The inference engine that implements the Tableaux algorithm ensures soundness, completeness, and decidability of the inference result. The inference engine ensures excellent performance than that of an inference engine that implements the inference process by a chain application of rules in a form of IF-THEN statements.
However, the inference engine that implements the Tableaux algorithm is disadvantageous in that a logic inference and a rule inference defined by a request from a user are not used together. This is because rules in a form of IF-THEN statements cannot be completely converted by a description logic expression and cannot be processed by the Tableaux algorithm.
However, in a system that uses a knowledge base, it is necessary to use a proper user-defined rule inference according to a request from a user or a request from an application using a knowledge base, in addition to a logic inference. To do so, a research is performed to extend the Tableaux algorithm to include a rule inference, but satisfactory performance and result are not shown.